One Trillion edges : Graph Processing at Facebook- Scale Tong Niu - - PowerPoint PPT Presentation

• 11. Juli 2019

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One Trillion edges : Graph Processing at Facebook- Scale

Tong Niu tong.niu.cn@outlook.com

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Outline

• Introduction
• Improvements
• Experiment Results
• Conclusion& Future Work
• Discussion

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Introduction

• Graph Structures(entities, connections)
• social networks
• Facebook manages a social graph that is composed of people,

their friendships, subscriptions, likes, posts, and many other connections. 1.39B active users in 2014 with more than 400B edges

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Introduction

• What is Apache Giraph?
• “Think like a vertex”
• Each vertex has an id, a value, a list of adjacent neighbors and

corresponding edge values

• Bulk synchronous processing(BSP)
• Break up to several supersteps(iteration)
• Messages are sent during a superstep from one vertex to another

and then delivered in the following supersteps

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Introduction

• What is Apache Giraph?

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Introduction

• What is Apache Giraph?
• Master – Application coordinator
• Assigns partitions to workers
• Synchronizes supersteps
• Worker – Computation, messaging
• Load the graph from input splits
• Does the computation/messaging of its assigned partitions

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• 1. Flexible vertex/edge based input
• Original input:
• All data(vertex/edge) need to be read from the same record and

assumed to the same data source

• Modified input:
• Allow loading vertex data and edges from separate sources
• Add an arbitrary number of data sources

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• 2. Parallelization support
• Original:
• Scheduled as a single MapReduce job
• Modified:
• Add more workers per machine
• Use local multithreading to maximize resource utilization

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• 3. Memory optimization
• Original:
• Large memory overhead because of flexibility
• Modified:
• Serialize the edges of every vertex into a bit array rather than using

native direct serialization methods

• Create an OutEdges interface that allow developers to achieve edge

stores

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• 4. Sharded aggregators
• global computation(min/max value)
• provide efficient shared state across workers
• make the values available in the next superstep

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• 4. Sharded aggregators
• Original:
• Use znodes in zookeeper to store partial aggregated data from workers,

master aggregate all of them and write result back to znode for workers to access it

• every worker has plenty of data that need to be aggregated
• Modified:

Randomly assigned to one of the workers

Distribute final values to master/workers

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K-Means clustering

In a graph application, input vectors are vertices, and centroids are aggregators.

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• 1. Worker phases
• Add preApplication() to initialize positions of centroids
• Add preSuperstep() to calculate the new position for each of the

centroids before next superstep

• 2. Master computation
• Centralized computation prior to every superstep that can communicate with

the workers via aggregators

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• 3. Composable computation
• Allows us to use different message types ,combiners and

computation to build a powerful k-means application

• 4. Superstep splitting
• For a message heavy superstep
• send a fragment of messages to the destinations and do a

partial computation during each iteration

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Experiment results

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Experiment results

• Giraph(200 machines) vs Hive(at least 200 machines)
• compare CPU time and elapsed time
• label propagation algorithm
• Weighted PageRank

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Conclusion & Future work

How a processing framework supports Facebook-scale production

• workloads. We have described the improvements to Giraph.

1.Determine a good quality graph partitioning prior to our computation. 2.Make our computation more asynchronous to improve convergence speed. 3.Leverage Giraph as a parallel machine-learning platform

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Discussion